The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
자유로운 생활 환경에서 음식 섭취량을 자동으로 모니터링하는 것은 여전히 해결해야 할 문제입니다. 본 논문에서는 하부 기관에서 피부 움직임을 감지하여 섭취 행동을 모니터링하는 압전 센서가 내장된 새로운 목걸이형 웨어러블 시스템을 제시합니다. 감지된 이벤트는 식품 분류에 통합됩니다. 스펙트로그램 기능을 사용하는 이전의 최첨단 압전 센서 기반 시스템과 달리 우리는 최적의 기능을 위해 시간 영역 기반 신호를 완전히 활용하려고 노력했습니다. 프레임 길이에 대한 수많은 평가를 통해 우리는 프레임 길이가 70샘플(3.5초)일 때 최고의 성능을 발견했습니다. 이는 씹는 순서가 식품 분류에 중요한 정보를 전달한다는 것을 보여줍니다. 실험 결과는 실제 시나리오에서 음식 섭취 감지 및 음식 분류를 위해 제안된 알고리즘의 타당성을 보여줍니다. 우리 시스템은 음식 섭취 감지에 대해 89.2%의 정확도를 제공하고 80.3개 식품 카테고리에 대한 식품 분류에 대해 17%의 정확도를 제공합니다. 또한, 우리 시스템은 스마트폰 앱을 기반으로 하며, 섭취한 음식 에피소드와 유형에 대한 실시간 피드백을 제공하여 사용자가 건강한 생활을 할 수 있도록 도와줍니다.
Ghulam HUSSAIN
Sungkyunkwan University
Kamran JAVED
Sungkyunkwan University
Jundong CHO
Sungkyunkwan University,North University of China
Juneho YI
Sungkyunkwan University
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Ghulam HUSSAIN, Kamran JAVED, Jundong CHO, Juneho YI, "Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 11, pp. 2795-2807, November 2018, doi: 10.1587/transinf.2018EDP7076.
Abstract: Automatic monitoring of food intake in free living conditions is still an open problem to solve. This paper presents a novel necklace-type wearable system embedded with a piezoelectric sensor to monitor ingestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classification. Unlike the previous state-of-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for optimal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classification. Experimental results show the validity of the proposed algorithm for food intake detection and food classification in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classification over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7076/_p
부
@ARTICLE{e101-d_11_2795,
author={Ghulam HUSSAIN, Kamran JAVED, Jundong CHO, Juneho YI, },
journal={IEICE TRANSACTIONS on Information},
title={Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System},
year={2018},
volume={E101-D},
number={11},
pages={2795-2807},
abstract={Automatic monitoring of food intake in free living conditions is still an open problem to solve. This paper presents a novel necklace-type wearable system embedded with a piezoelectric sensor to monitor ingestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classification. Unlike the previous state-of-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for optimal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classification. Experimental results show the validity of the proposed algorithm for food intake detection and food classification in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classification over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.},
keywords={},
doi={10.1587/transinf.2018EDP7076},
ISSN={1745-1361},
month={November},}
부
TY - JOUR
TI - Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System
T2 - IEICE TRANSACTIONS on Information
SP - 2795
EP - 2807
AU - Ghulam HUSSAIN
AU - Kamran JAVED
AU - Jundong CHO
AU - Juneho YI
PY - 2018
DO - 10.1587/transinf.2018EDP7076
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2018
AB - Automatic monitoring of food intake in free living conditions is still an open problem to solve. This paper presents a novel necklace-type wearable system embedded with a piezoelectric sensor to monitor ingestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classification. Unlike the previous state-of-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for optimal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classification. Experimental results show the validity of the proposed algorithm for food intake detection and food classification in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classification over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.
ER -